Owasp

Agent goal hijacking is the OWASP ASI01 risk where an attacker redirects an AI agent from its intended objective toward a malicious or unauthorized outcome. The practical danger is not a weird answer; it is an autonomous workflow using tools, identity, memory, or production APIs to do the wrong thing. What Is Agent Goal Hijacking? Agent goal hijacking is an attack where malicious input changes an AI agent’s objective, plan, or decision path so the agent pursues the attacker’s goal instead of the user’s intended goal. OWASP ranks this as ASI01 in the OWASP Top 10 for Agentic Applications 2026, a peer-reviewed framework built with input from more than 100 experts. The risk matters because modern agents do more than generate text: they browse pages, read tickets, query RAG indexes, call APIs, update records, create pull requests, and send messages. A hijack can start with one hostile paragraph in a web page, PDF, email, or tool response, then unfold across several steps as the agent plans and acts. The core takeaway is simple: agent goal hijacking turns untrusted content into control-plane input for software that can take real actions. ...

OWASP agentic applications security is the practice of limiting what AI agents can decide, access, remember, execute, and delegate. The 2026 OWASP Agentic Top 10 gives developers a checklist for shipping agents that call tools, persist state, and act across real systems without turning autonomy into uncontrolled production risk. What Is the OWASP Top 10 for Agentic Applications 2026? The OWASP Top 10 for Agentic Applications 2026 is a security framework for AI systems that plan, choose actions, call tools, use memory, and coordinate with other agents. OWASP released it on December 9, 2025, after work from more than 100 industry experts, researchers, and practitioners. The list is different from the OWASP LLM Top 10 because it focuses on agent behavior, not only model input and output. A chatbot can give a bad answer; an agent can approve a refund, run a shell command, update a CRM record, leak a token through a tool call, or ask another agent to continue the mistake. For developers, the useful shift is to treat each agent as a production actor with identity, permissions, state, budget, and failure modes. The takeaway: secure agentic applications by controlling autonomy, not just prompts. ...

Released on April 2, 2026, the Microsoft Agent Governance Toolkit is the first open-source runtime security framework to address all ten risks on the OWASP Agentic AI Top 10. Shipped under the MIT license, it provides deterministic policy enforcement at the agent action layer with less than 5ms overhead per evaluated action. As the agentic AI security market grows from a projected $1.65 billion in 2026 toward an estimated $13.52 billion by 2032 at roughly 42% CAGR, this toolkit arrives at exactly the moment enterprises need a vendor-neutral, community-owned standard for governing what their AI agents are actually permitted to do. ...